From 8458596b64a3b02810565bd21ec70b697b22f7cb Mon Sep 17 00:00:00 2001 From: lrrbrody Date: Tue, 27 Sep 2016 16:11:41 -0400 Subject: [PATCH 1/2] Lauren Romine class 7 assignment --- .DS_Store | Bin 0 -> 6148 bytes .gitignore | 4 ++++ Class 7 Instructions.Rmd | 18 ++++++++++++------ Rplot.png | Bin 0 -> 18910 bytes Rplot01.png | Bin 0 -> 18489 bytes instructor_activity_wide.csv | 17 ++++++++++++++++- 6 files changed, 32 insertions(+), 7 deletions(-) create mode 100644 .DS_Store create mode 100644 .gitignore create mode 100644 Rplot.png create mode 100644 Rplot01.png diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..339143ff38a038560e6e97803561b73c88116e64 GIT binary patch literal 6148 zcmeHKu}%U(5Pgf7K#U261%<_yRwP!oCxQuu4Ss+MSZGepc!06H&+w!C0RKkc>}2BE6k4y^O%*S&Zcrk-VNubvsm%|0&k4GvNyv$XEf#bRXML4SMs(wgCWi_z!kAq ze+KtiVH|~7EJKYsYu4wmwQ|tn{f8DzYi0_V0;a&uDZo8jtaYGhttns%m;!4B$Q(i!)0ToSQRR61Nde7IQI z#S@AP)tTRi;c$tfwWfe6P*z}1FFUgT4}U-Zmq~VI3YY@_N&(j#To3wuq`0@%9!~aJ sPrs#$$+$vsO<^Z&#T+YJahD#9aW5TW60uNZgk~QCK?Z9~fj?E?8%|?l2mk;8 literal 0 HcmV?d00001 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..5b6a065 --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +.Rproj.user +.Rhistory +.RData +.Ruserdata diff --git a/Class 7 Instructions.Rmd b/Class 7 Instructions.Rmd index 5ae641a..4719ed1 100644 --- a/Class 7 Instructions.Rmd +++ b/Class 7 Instructions.Rmd @@ -59,7 +59,7 @@ instructor_data <- spread(data_long, variables, measure) ##Now we have a workable instructor data set!The next step is to create a workable student data set. Upload the data "student_activity.csv". View your file once you have uploaded it and then draw on a piece of paper the structure that you want before you attempt to code it. Write the code you use in the chunk below. (Hint: you can do it in one step) ```{r} - +student_wide <- spread(student_activity, variable, measure) ``` ##Now that you have workable student data set, subset it to create a data set that only includes data from the second class. @@ -75,7 +75,7 @@ student_data_2 <- dplyr::filter(student_data, date == 20160204) Now subset the student_activity data frame to create a data frame that only includes students who have sat at table 4. Write your code in the following chunk: ```{r} - +student_wide <- spread(student_activity, variable, measure) ``` ##Make a new variable @@ -89,7 +89,7 @@ instructor_data <- dplyr::mutate(instructor_data, total_sleep = s_deep + s_light Now, refering to the cheat sheet, create a data frame called "instructor_sleep" that contains ONLY the total_sleep variable. Write your code in the following code chunk: ```{r} - +instructor_sleep <- dplyr::select(instructor_data, total_sleep) ``` Now, we can combine several commands together to create a new variable that contains a grouping. The following code creates a weekly grouping variable called "week" in the instructor data set: @@ -100,7 +100,7 @@ instructor_data <- dplyr::mutate(instructor_data, week = dplyr::ntile(date, 3)) Create the same variables for the student data frame, write your code in the code chunk below: ```{r} - +student_wide <- dplyr::mutate(student_wide, week = dplyr::ntile(date, 3)) ``` ##Sumaraizing @@ -111,13 +111,14 @@ student_data %>% dplyr::summarise(mean(motivation)) #That isn't super interesting, so let's break it down by week: -student_data %>% dplyr::group_by(date) %>% dplyr::summarise(mean(motivation)) +student_data ``` Create two new data sets using this method. One that sumarizes average motivation for students for each week (student_week) and another than sumarizes "m_active_time" for the instructor per week (instructor_week). Write your code in the following chunk: ```{r} - +student_week <- student_wide %>% dplyr::group_by(week) %>% dplyr::summarise(mean(motivation)) +instructor_week <- instructor_data %>% dplyr::group_by(week) %>% dplyr::summarise(mean(m_active_time)) ``` ##Merging @@ -131,6 +132,11 @@ merge <- dplyr::full_join(instructor_week, student_week, "week") Visualize the relationship between these two variables (mean motivation and mean instructor activity) with the "plot" command and then run a Pearson correlation test (hint: cor.test()). Write the code for the these commands below: ```{r} +plot(merge$`mean(m_active_time)`, merge$`mean(motivation)`) +cor.test(merge$`mean(m_active_time)`, merge$`mean(motivation)`) + ### Or the opposite, for the same correlation results but different plot view. ### + plot(merge$`mean(motivation)`, merge$`mean(m_active_time)`) + cor.test(merge$`mean(motivation)`, merge$`mean(m_active_time)`) ``` diff --git a/Rplot.png b/Rplot.png new file mode 100644 index 0000000000000000000000000000000000000000..b3a539607788f2abfff3ea4f7605d7f5e216c212 GIT binary patch literal 18910 zcmeIag;UjQ_%BKd2-4jtohscWCEd~psC0M70+EnLKtKgVLRz{zMY=(xLmKJxE*9?J zxpV)3d*{rVVb7k;Uh5lgJkO_IB2<;1KDbYM9}W)gfxMiwIvgB)9r))t>OJrqC3fm` zI5?ySucV|@<)x&kRh{fDU)fl|!SP2$X&JsG*TNUAbGD+RAJnjU9P08#>;c1rcn~1r2UB_m`5)C?Wvj@j`fzWi;kP$^51?i`#+nhW;7q*FFg;4m zBwFz|Sam%zcPmxsQR=JO(s_$rpTmqlAG~*Kto|Uc*L>AYdWR2B_`aB(i079xBqm&h zH8up2MRYG)M~#BjFkcCQR|Va@^7(>PtGDsg*xYqy(RW!20%}Jpv+~t1=;1N&dwE)Y zZtz~`@2JE!^(jxx*1vO$w{v<`FPTrYIg9#5y|Vnx_G=^@Sxe(}crtcOf5%^0j0^6P zapSCKueZLguY5F1bDhg7b@*d?ooU2n@`^Nd3#DL)T(}w zWYp>mo*AEQUGki;UcUak=>J=1|GQI{et%t=QrotTuCu!qsqMXG!oA_tj))G5L&yE! zI#{xPNj<4+-;NB0_3Sw?Weby$&tIQulPE_?7aj#4K`Pi97=4+pIvWOjdwmCd2Yp)% z5#9;)Wh^%?aX3&p8+V?(J|xv&@Fw&m{%*CAWL>C0LuW%kN$AwdyQF5ybHIp!YA&fZ z8JJYGbwQZP`9XvN)0}EG@4}#wLmNKgq7>oi*|nCI=7vK8GljPOGyX;iytnjHFWx5b zYMxf>9A?uds;sEL!qyPdz{@WkmB50O=elLtB3xWjjOE*vsc-%?pZ7)38xgeIdN4rX zuJ|+FjPx5XOVazz_3$smVckm6FEt?3w)$kqf98*3j?#GQu<)L9JWapAXl@kS-@f4Z z6>^>MQ2Am)Qd8Y%zqd%T{hD~@lMVMeR1@3`=sfd$HVVxbYr&Nxp0j=pps80 zqiNxf%ebicO#z%$ls|k{rjM4Y$+bNkoH(4kw1lPy{7wc+3jWy7(@1)OH8zO|+ypkc zg!JGEMM4Q_c${hzS&zUzWMp`WkAG?9;Z%Icwr-coB6*7 zVnQ;j+{Ps5r$)So5lg_TE!UgC%%Ji?IF8TWsD*27sSUESI*=B}?`XC$TT_lf#;fG? 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+75,7 @@ student_data_2 <- dplyr::filter(student_data, date == 20160204) Now subset the student_activity data frame to create a data frame that only includes students who have sat at table 4. Write your code in the following chunk: ```{r} -student_wide <- spread(student_activity, variable, measure) +student_table4 <- dplyr::filter(student_wide, table == 4) ``` ##Make a new variable